A Distribution Network Reconfiguration Algorithm Based on Hopfield Neural Network

On the base of Hopfield neural network, the minimum of feeder looses is treated as the target function. Because the distribution network is radical, we put forward a method for deciding each node's in-degree by using Hopfield neural network. According to each node's in-degree, it can be ea...

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Vydané v:2008 Fourth International Conference on Natural Computation Ročník 3; s. 9 - 13
Hlavní autori: Weixin Gao, Nan Tang, Xiangyang Mu
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2008
Predmet:
Artificial intelligence
Computer networks
Distributed computing
Distribution Power Network
Equations
Hopfield Neural Network
Hopfield neural networks
Mathematical model
Power supplies
Power system reliability
Reconfiguration
Switches
Voltage
ISBN:9780769533049, 0769533043
ISSN:2157-9555
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Abstract On the base of Hopfield neural network, the minimum of feeder looses is treated as the target function. Because the distribution network is radical, we put forward a method for deciding each node's in-degree by using Hopfield neural network. According to each node's in-degree, it can be easily determined whether the line will be used or not. So the state of switch and the scheme of reconfiguration can be decided correspondingly. The energy function of Hopfield neural network is given in this paper. The problems of satisfying the restriction of radial supplying and minimizing the feeder power looses are considered in the energy function simultaneously. The energy function even takes the problem that some lines may have no switches into consideration. By calculating an IEEE distribution network with three power sources, it can be found that the calculated result of Hopfield neural network is somewhat similar to the result obtained by the more complex genetic algorithm. Since the former is to calculate a group of differential function, so the calculation time needed is comparatively less.
AbstractList On the base of Hopfield neural network, the minimum of feeder looses is treated as the target function. Because the distribution network is radical, we put forward a method for deciding each node's in-degree by using Hopfield neural network. According to each node's in-degree, it can be easily determined whether the line will be used or not. So the state of switch and the scheme of reconfiguration can be decided correspondingly. The energy function of Hopfield neural network is given in this paper. The problems of satisfying the restriction of radial supplying and minimizing the feeder power looses are considered in the energy function simultaneously. The energy function even takes the problem that some lines may have no switches into consideration. By calculating an IEEE distribution network with three power sources, it can be found that the calculated result of Hopfield neural network is somewhat similar to the result obtained by the more complex genetic algorithm. Since the former is to calculate a group of differential function, so the calculation time needed is comparatively less.
Author Weixin Gao
Nan Tang
Xiangyang Mu
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  organization: Shaanxi Key Lab. of Oil-Drilling Rigs Controlling Tech., Xian Shiyou Univ., Xian
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Snippet On the base of Hopfield neural network, the minimum of feeder looses is treated as the target function. Because the distribution network is radical, we put...
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StartPage 9
SubjectTerms Artificial intelligence
Computer networks
Distributed computing
Distribution Power Network
Equations
Hopfield Neural Network
Hopfield neural networks
Mathematical model
Power supplies
Power system reliability
Reconfiguration
Switches
Voltage
Title A Distribution Network Reconfiguration Algorithm Based on Hopfield Neural Network
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